LIMSSR reformulates incomplete multimodal learning as LLM-driven sequence-to-score reasoning with prompt-guided imputation and mask-aware aggregation, outperforming baselines on action quality assessment without complete training data.
Cross-modal adapter for text-video retrieval
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.CV 3representative citing papers
READ recurrent adapters with partial video-language alignment via optimal transport outperform standard fine-tuning on low-resource temporal grounding and summarization tasks.
citing papers explorer
-
LIMSSR: LLM-Driven Sequence-to-Score Reasoning under Training-Time Incomplete Multimodal Observations
LIMSSR reformulates incomplete multimodal learning as LLM-driven sequence-to-score reasoning with prompt-guided imputation and mask-aware aggregation, outperforming baselines on action quality assessment without complete training data.
-
READ: Recurrent Adapter with Partial Video-Language Alignment for Parameter-Efficient Transfer Learning in Low-Resource Video-Language Modeling
READ recurrent adapters with partial video-language alignment via optimal transport outperform standard fine-tuning on low-resource temporal grounding and summarization tasks.
- SRL-CLIP: Efficient CLIP Video Adaptation via Structured Semantic Role Labels